Enhanced Detection Performance of Indoor GNSS Signals Based on Synthetic Aperture
Bibliographic record
Abstract
There is an intense interest in detecting and processing global navigation satellite system (GNSS) signals indoors and in urban canyons by handheld devices where the signal is very weak and the fading is predominantly Rayleigh. To overcome these signal limitations, long coherent integration is normally used, which significantly increases the mean acquisition time (MAT) for GNSS applications. Moving the antenna arbitrarily while collecting GNSS signals is generally avoided as it temporally decorrelates the GNSS signal and limits the coherent integration gain. However, this decorrelation also provides diversity gain in a dense multipath environment. In this paper, the coherent integration loss due to antenna motion in a Rayleigh-fading channel is quantified. This is applicable to a variety of situations, including vehicle passengers and pedestrians moving in urban canyons and indoors. Then, an optimal approach for detecting GNSS signals utilizing a single moving antenna operating as a Synthetic Aperture based on the Estimator-Correlator (SAEC) is presented. The SAEC algorithm takes into account the receiver motion and multipath fading model. The performance of the moving receiver with the SAEC method is compared with a static and a moving receiver, which directly implement coherent integration. The performance of the Synthetic Aperture based on the suboptimal Equal-Gain (SAEG) combiner is also presented. As shown theoretically and experimentally, for given target-detection performances in terms of the probability of false alarm <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FA</sub> and the probability of detection <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">PD</i> , the required signal-to-noise ratio to attain the above performances can be significantly reduced through the application of the SAEC while the receiver is moving. This results in a further reduction of the MAT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".